Using Non-Linear Support Vector Machines for Detection of Activities of Daily Living
作者： Muhammad Ahmer , MZ Shah , Syed M Zafi S Shah , Syed Shah , M Shehram
DOI: 10.17485/IJST/2017/V10I36/119182
关键词:
摘要: Activities of Daily Living (ADL) refers to different daily routine type activities which includes walking, running, jogging, standing, sitting etc. Recognition ADLs has been considerable interest researchers for health assessment purposes. Furthermore, since more and people choose live alone in their house. ADL recognition serves as the first step towards developing a monitoring system such people. This work proposes an algorithm that can be used perform detection using three types data from inertial sensors (accelerometer, gyroscope orientation) captured smart phone non-linear Support Vector Machines. We have representative dataset named MobiACT extracting sensor readings 10s window, Autoregression modeling model we detected six Machine. achieve overall accuracy 97.45%. The given method tested proven outperform other algorithms purpose activity recognition. Keywords: Living, Autoregressive Modelling, Inertial Sensor, Mobiact
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